For the most part, robo-adviser platforms look broadly similar in the following ways.

(1) USA-centric Portfolio Asset Allocation

Many robo-advisers use Exchange Traded Funds (ETFs”) which track US market indices covering big to small
cap stocks or US industry sectors such as financials, technology, consumer staples, utilities and so on. By
contrast, these same robo-advisers allocate only a small proportion of assets to international or emerging
markets.

(2) Human Managed Portfolios

Most robo-adviser platforms offer 5 to 30 pre-assembled portfolios. Each or a group of these preassembled
portfolios are linked to a set number of pre-labelled risk buckets, e.g., low risk, moderate risk and high risk in
the simplest presentation. Through a risk profiling shopfront process, the individual client is classified into one of
the pre-labelled risk buckets together with others. The pre-assembled
investment portfolio linked to that risk
bucket is then assigned to that individual client.

(3) Constant Rebalancing Rule

Several robo-adviser platforms use the constant – mix rebalancing technique. Let’s imagine that if a portfolio
is pre-assembled with 50% in Equity and 50% in Bonds. Then, let’s say that the equity market
is in a bull phase and equity asset prices rise in general such that the portfolio asset allocation shifts to 60% in Equity and 40%
in Bonds. The subsequent rebalancing rule is to sell 10% in Equity and buy 10%
in Bonds to achieve the optimization, that is, by shifting back to the original asset allocation.

At PIVOT, we decided to do better. Compared with most other robo-adviser platforms, we use a
proprietary AI-driven method called F.A.M.E. – or Factor Analytics Machine Learning Engine, to drive
personalized, dynamic and real-time global investment portfolio construction and the subsequent
rebalancing forward to a superior risk-adjusted portfolio.

This whitepaper outlines how our F.A.M.E. methodology analyzes different investment markets without human
bias, drives portfolio optimization based on factor model analyses, and executes dynamic
rebalancing
separately for each and every client, and not as a group risk-labelled bucket. And as an innovative fully
AI-driven digital investment manager, we do this for each and every client – whether the
invested amount
is one dollar or one million. Our F.A.M.E. methodology works 24/7 as the global
investment markets turn and
does not discriminate.

Section 2: Pivot’s Factor Modelling with Machine Learning Analytics

Factor Modelling is a financial model that employs single or multiple factors in its calculations to explain
market phenomena and/or equilibrium asset prices. The factor model can be used to explain or estimate the
probable asset price/volatility of either an individual security or a portfolio of securities. This is
traditionally
executed using linear regression modelling with Least-Squares Fit to minimize the cost
function and enhance
forecasting accuracy.

Single-Factor Model

In traditional finance theory, CAPM (Capital asset Pricing Model) is viewed as a single factor model which
describes the relationship between systematic risk and expected return for assets with the formula:

where:

is the expected return for single capital asset (stocks, indices, bonds, etc).

is the risk free rate.

is the estimated return of markets.

However, instead of using the global return as a predictor, we may choose several distinct factors as
predictors - such as PMI, EPS or 20 Days Moving Averages to forecast the impact on the underlying
asset
price behaviour

Multi-Factor Model

Unlike the single factor model which uses only one variable to describe the future returns of a portfolio or
stock, the Multi-Factor Model incorporates more information as part of the decision science. For example, the
Fama–French model uses three variables: (1) market risk; (2) outperformance of small
versus big companies;
and (3) outperformance of high book/market value versus low book/market value securities.

The Fama-French three factor model can be formulated as:

where:

stands for Small (Market Cap) minus Big Cap

stands for High (Book to Market) minus Low

PIVOT’s F.A.M.E. Algorithm

In the last 5 years, robo-adviser platforms have emerged across the globe. Most of them feature:

Suitability

Scientific Logic

Accuracy

With the PIVOT F.A.M.E. algorithm structured in the following steps:

Calculate the importance of factors by using Feature Engineering. PIVOT derives the most significant
factors across different time periods (90 days, 180 days, 10 years, etc)

Assess the Market Forecasts via the Single Factor Model using different algorithms (Classification Model)B1. Logistic Regression: In statistics, the logistic model is used to model the probability of a certain
classification such as Bull / Bear/ Neutral of a single assetB2. Support Vector Machine: Support Vector Machine (SVM) is primarily a classifier method that
performs classification tasks by constructing hyperplanes (via using different Kernal functions) in a
multidimensional space that separates cases of different class labels.B3. Decision Tree and Random Forest: A decision tree is a decision support tool that uses a tree-like
model of decisions and their possible consequences, whereas the Random Forest method consists of a
large number of individual decision trees that operate as an ensemble. Each individual tree in the
Random Forest spits out a class prediction and the class with the most votes becomes our model’s
prediction.

Assemble the results via step (B) into final estimation for each specific factor

C1. Equal Weights

C2. Historical Accuracy Rate

C3. Historical Information Correlation (IC):

C4. Max IC: and

C5. Neighborhood Component Analysis:

By timing the weight of a factor and its estimation assembled in step (C), we get the expectation value
using probability theory (i.e., get the significance contribution of a single factor) and when we sum up the
values among all factors, it gives us the final result of the estimation for the specified asset.

Section 3: Portfolio Optimization on Pivot’s Factor Model

The F.A.M.E. model can be used to estimate different asset class in different timing. Hence, it can generate a
trading signal for long / short decisions, or it could change the parameter settings in traditional asset
allocation theory.

In MPT, we can use the advisory provided by F.A.M.E. to uplift the Lower Bound (if the estimation is bull) and
decrease the Upper Bound (if the estimation is bearish) as the parameter adjustment and for Risk-Parity, we
could either adjust the forecasted risk of each asset or change the numerator from 1 to the ML score of the
asset.

Here is an example to show how F.A.M.E. works on the Markowitz model & Risk Parity model
to improve the
performance of the Singapore Straits Times stock index (STI), a capitalizationweighted stock market index
that is regarded as the benchmark index for the Singapore stock market.

Method 1 – Machine Learning ML
Enhanced Frontier Allocation

By applying the Factor Model to change
the UB (Upper Bound) / LB (Lower Bound)
settings of the 30 component stocks, we
get the result shown on the right.

Back Testing: 2009 to 2019 Aug
- Return: The new enhanced
portfolio
generates 164% total
return while STI
generated 73%.

- Risk: The maximum drawdown of new
enhanced portfolio is 20.67% since 2009
while the STI maximum drawdown is up
to 28.45%

Method 2 – Machine Learning ML
enhanced Risk-Neutral
Allocation

By applying the Factor Model
and
changing the numerator of risk
parity for each asset class from 1 to the
ML generated scores, the new allocation
is deemed to be positively correlated to
the ML scores and negatively correlated
to volatility.

Back Testing : 2009 to 2019 Aug
- Return: The new enhanced
portfolio will
generate 167% total returns while the STI
generated
73%.

- Risk: The maximum drawdown of the new enhanced portfolio is 24.79% since 2009 while the STI over the
same timeline generated a maximum drawdown up to 28.45%.

Section 4: Personalized Solution – Dynamic Advisory and Rebalancing

In the wealth management business, most of the advisory provided is based on predetermined
modules (e.g., matching reference portfolios to different risk tolerance levels). A simple way to
fix the reference portfolios is by way of a range of asset allocations

Our F.A.M.E. algorithm, however, combines both customer characteristics such as Risk Tolerance, Age,
Investment Time Horizon and Market Conditions when an investor is seeking a recommended asset
allocation. The customer characteristics will affect the parameter settings of the Debt/Equity Ratio, Investment
Asset Class Coverage and UB/LB for each asset class.

Advantages of Dynamic Asset Allocation & Rebalancing: Dynamic asset allocation and rebalancing help
to position the appropriate market exposure for each different asset class. To formulate the overall
investment strategy, the F.A.M.E. model must consider several factors including portfolio deviation and
client holdings, market conditions, transaction costs (including transaction fees and transaction time lags)
for rebalancing.

With the Dynamic Asset Allocation and Rebalancing function, F.A.M.E. helps to control the downside risks
versus major market indices so the client will not panic during any black-swan event.

Examples of Risk Control under Panic Market Conditions

- 2008: The global market dropped more than 50% due to high inflation and Lehman Brother’s credit crisis
while Pivot’s portfolio only suffered a drawdown of around 15% by shifting asset allocation towards US
government bonds.

- 2018: The global market dropped more than 20% because of the US/China trade war while Pivot’s portfolio
showed better wealth protection, dropping by only 10% in NAV